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Navigational Perception in Legal Information Environments

Navigational Perception in Legal Information Environments

Jun 19, 2026 By SystemsDigest In SystemsDigest
Legal digital environments operate within a unique informational context where clarity, trust, and accessibility must coexist with complexity. Unlike many commercial websites that focus primarily on transactions or engagement, legal platforms often serve as information systems that help users understand unfamiliar situations, evaluate options, and make important decisions. To support this process, legal environments rely on layered information architecture, where content is organized into interconnected informational nodes.
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speedscale

Build WireMock mappings fast from real traffic

Jun 18, 2026 By Ken Ahrens In Speedscale
I’m a big fan of service mocking. I’ve been working in and around software for about 25 years, and one thing never changes: when you sit down to work on your code, you almost never have everything available. The database, the third-party API, the message queue, the service two teams over. Something’s missing. So you’ve got to stub it out or mock it out and keep moving.
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confluent

Meeting Data (and Analytics) Engineers Where They Are: Introducing the dbt Adapter for Confluent Cloud

Jun 18, 2026 By Zander Matheson In Confluent
dbt is the most commonly used tool by data engineers to define SQL transformations (as models), write tests, generate documentation, and deploy through CI/CD and now it’s available with Confluent Cloud too! The magic of dbt is that it brings the engineering rigor to modern data work and data engineering, regardless of the underlying compute source - Snowflake, BigQuery, Databricks, Redshift or Confluent. You can find out more about the launch in our Q2 Confluent Cloud Launch post and the keynote.
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Ready Set Code! The Telemetry Tsunami

Jun 18, 2026 By Snowflake Inc. In Snowflake
Welcome to Ready Set Code! The game show where data engineers face off to prove who can build faster. In today's episode, "The Telemetry Tsunami," three contestants face a massive flood of nested JSON telemetry data. Their mission: flatten the arrays, join it to customer tables, and deploy a secure automated pipeline. Who will separate themselves as a data driver vs. a data downer? Find out now! Type Less. Build More.
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The Hidden Cost of AI Testing: Stop Burning LLM Tokens in Your CI/CD Pipeline

Jun 18, 2026 By Perforce In Perforce
AI testing against live LLM APIs can quietly drive massive token costs across development, QA, and CI/CD pipelines. Every test execution consumes real tokens—at production rates—creating hidden, variable costs that scale with your AI adoption. In this video, discover how leading enterprises are eliminating LLM token spend using service virtualization. Learn how BlazeMeter intercepts API calls, simulates realistic AI responses (completions, embeddings, and large payloads), and enables full-scale testing without invoking live models.
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Streaming highlights from Databricks Data + AI Summit

Jun 18, 2026 By LensesIO In Lenses
Join Tun Shwe and Jeremy Frenay as they stream live from the floor of the Databricks Data + AI Summit! They’ll break down the biggest announcements, key takeaways, and cutting-edge trends shaping the intersection of AI and data streaming. Register to get an insider look at the future of data AI streaming.
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keboola

Guessing AI vs. Verifiable AI: Why the Difference Matters in Finance

Jun 18, 2026 By K In Keboola
I asked Claude what the cash position would be at year-end. The answer was about 30% off. A CFO said this at a finance leaders breakfast in Prague. Almost every CFO in the room had a version of the same story. The problem is not the model. Claude is not bad at maths. The problem is what the model was reasoning over - raw financial data with no governed definitions, no intercompany rules, no agreed methodology for what 'cash position' means at that specific company.
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